DOCSLIB.ORG

Measuring Perceived Color Difference Using YIQ Color Space

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Measuring Perceived Color Difference Using YIQ Color Space Programación Matemática y Software (2010) Vol. 2. No 2. ISSN: 2007-3283 Recibido: 17 de Agosto de 2010 Aceptado: 25 de Noviembre de 2010 Publicado en línea: 30 de Diciembre de 2010 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLOGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen: En este trabajo varias fórmulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las fórmulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes fórmulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las fórmulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real. Abstract: An alternative color difference formulas are presented for measuring the perceived difference between two color samples defined in YIQ color space. The classical color difference formulas such as CIELAB, CIELUV and their derivatives require many arithmetic transformations of the input values commonly given using red, green and blue components, making these formulas cumbersome to implement on mobile devices. The alternative formulas based on YIQ color space presented in this work are lightweight and can be quickly calculated, even in real-time. The comparison is made between the newly introduced formulas and the classical ones using two different sets of experiments. The first set of experiments is focused on evaluating the perceived difference of colors using different formulas for picking colors. The second set of experiments allows benchmarking of the color difference formulas to evaluate their performance when processing images. The experimental results show that the newly introduced formulas are close in perceived terms to CIELAB and CIELUV but are significantly faster, making them good candidates for measuring color difference on mobile devices and applications even in real-time. KEYWORDS: COLOR DIFFERENCE, COLOR METRIC, YIQ, PERCEPTUAL UNIFORMITY, COLOR SPACE 28 Yuriy Kotsarenko, Fernando Ramos Introduction There are many applications where two or This fact led to the development of more more colors need to be compared for some perceptually uniform color spaces described purpose. The examples of such applications later on in this work. In addition, not all colors include but are not limited to stereo vision visible to human eye can be modeled by the algorithms, recognition algorithms used in RGB color space [1]. The RGB color space surveillance, textile industry and image itself can be defined in many ways depending processing. As the number of mobile devices on the three chosen primaries making the is growing, there is an increasing range of space dependent on the particularly used devices, each with unique set of processing device. In the context of this work, the values capabilities. In the typical software of RGB color space are specified according applications running on the aforementioned to sRGB standard described in Rec. 709 [2]. systems the colors are usually defined by In an informal article “Colour Metric” their respective red, green and blue values, posted on CompuPhase web site on Internet also called RGB color space. A comparison [3], Thiadmer Riemersma suggested a of two sample colors usually involves variation to the equation (1), which calculating the distance or difference apparently gives better results in perceptual between them. If the sample colors have their terms and justifies it by the fact that the respective coordinates specified as modification is used in their products such as ( r , g , b ) and ( r , g , b ), then the 1 1 1 2 2 2 EGI, AniSprite and PaletteMaker. The difference between the two colors can be proposed modification is the following: calculated as: r 2 2 255 r 2 Eri 2 r 4 g 2 b 2 2 2 256 256 (2) E r r g g b b (1) rgb 2 1 2 1 2 1 E r 2 g 2 b 2 (1.1) rgb where r r1 r 2 2 and r1, g1, b1, r2, g2 The values of , , and , , are and b2 values are given in the range of [0, specified in the range of [0, 1]. In some 255]. According to Thiadmer Riemersma, the performance critical applications, the square formula was derived from several tests using 2 a program which displayed 64 colors palette root is omitted and the value of Ergb is and a sample color, that user had to match to used instead. an existing color on the palette; in addition, The equation (1.1) is both easy to the program also suggested a matching color implement and fast to compute. However, the depending on one of the available color arbitrary variations of values in RGB space difference equations. are not perceived as uniform by human eye. Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications 29 The equation (2) has an elegant form the equation (4) are specified according to and can actually accept integer values for Rec. 709 standard [7], but other RGB components – a good point for mobile specifications exist [2]. applications, but it lacks experimental data to The CIE 1931 XYZ color space is justify the selection of weight coefficients and primarily used for specifying other the program used by the author to deduce perceptually uniform color spaces. One of the equation (2) displayed a very limited such color spaces that is also considered a palette to the users, making the reliability of CIE standard - the CIE 1976 L*, a*, b* the proposed formula inconclusive. (CIELAB) color space, which can encompass A color space that can encompass all all the colors visible to human eye and is the colors visible to the human eye has been somewhat perceptually uniform [2], [9]. The proposed - the CIE 1931 XYZ color space, coordinates in CIELAB color space can be which is considered to be CIE standard. The calculated from CIE XYZ values as the selection of XYZ functions and the following [2], [4]: development of the color space were Y described by J. Schanda [4]. The coordinates * 116 fL 16 (5) Y specified in RGB color space can be n transformed to CIE XYZ as the following [2], X Y * 500 fa f (6) [5]: X Y n n X .0 412453 .0 357580 .0 180423 r * Y Z Y .0 212671 .0 715160 .0 072169 g (3) 200 fb f (7) Z .0 019334 .0 119193 .0 950227 b Yn Z n where X, Y and Z are the new coordinates in t 31 , t 6 29 3 2 CIE XYZ color space and r, g and b are the tf 1 29 4 (8) t , otherwise linear RGB coordinates [5], [6]. The 3 6 29 transformation from non-linear r , g and b where f(t) is a temporary transform function, coordinates to their linear counterparts is Xn, Yn, and Zn are the coordinates of the made by applying gamma correction [2], [6]: particular white point used in the calculations. According to Rec. 709 [5] the white point D65 1 VsRGB VsRGB .00, 03928 has the coordinates of X =0.9505, Y =1 and 12.92 n n L 4.2 (4) V sRGB .0 055 Zn=1.0891 (calculated from their x and y .0, 03928 VsRGB 1 .1 055 coordinates of 0.3127 and 0.329 respectively on the CIE chromaticity diagram [1], [6], [7] where VsRGB should be replaced by the [9]). The values of a* and b* can vary in [- r , g and b values to obtain the linear 100, 100] range for typical images, while the values of r, g and b. The coefficients used in value of L* is usually defined in the range of 30 Yuriy Kotsarenko, Fernando Ramos [0, 100]. The color difference between a pair The simplified equation using the of two samples can therefore be calculated aforementioned cylindrical values of C* and as: h* according to J. Schanda [4] is the following: * * 2 * 2 * 2 (9) ELab L a b 2 2 2 (15) ELCH LCH There are many color difference formulas derived from CIELAB color difference (for The equations (9), (10) and (15) represent instance, several variants are measured in the human perception of colors more closely [10]) and in many cases they can be (unless CIELAB is not really perceptually generalized to the following form [10], [11]: uniform [9]) and they provide a way of calculating color differences for all colors that 2 2 2 L C * H * * Egen R (10) can be seen by human eye (as opposed to kLL S kCC S kHH S RGB-based color difference formulas where R according to M. R. Luo, G. Cui described earlier). However, they require and B. Rigg [11] is an interactive term three transformations from original RGB between chroma and hue differences (in coordinates to CIELAB and equation (15) many variants it is set to zero [10]), kL, kC, requires further transformation of the and kH are parameters given by the user that coordinates to cylindrical coordinate system, according to M.
Load more

Recommended publications
  • An Improved SPSIM Index for Image Quality Assessment
    S S symmetry Article An Improved SPSIM Index for Image Quality Assessment Mariusz Frackiewicz * , Grzegorz Szolc and Henryk Palus Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; [email protected] (G.S.); [email protected] (H.P.) * Correspondence: [email protected]; Tel.: +48-32-2371066 Abstract: Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications. Keywords: image quality assessment; image databases; superpixels; color image; color space; image quality measures Citation: Frackiewicz, M.; Szolc, G.; Palus, H. An Improved SPSIM Index 1. Introduction for Image Quality Assessment. Quantitative domination of acquired color images over gray level images results in Symmetry 2021, 13, 518. https:// the development not only of color image processing methods but also of Image Quality doi.org/10.3390/sym13030518 Assessment (IQA) methods.
    [Show full text]
  • COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING
    COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING Color space A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components (e.g. RGB and CMYK are color models). However, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with little connection to the requirements of any given application. Adding a certain mapping function between the color model and a certain reference color space results in a definite "footprint" within the reference color space. This "footprint" is known as a gamut, and, in combination with the color model, defines a new color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model. In the most generic sense of the definition above, color spaces can be defined without the use of a color model. These spaces, such as Pantone, are in effect a given set of names or numbers which are defined by the existence of a corresponding set of physical color swatches. This article focuses on the mathematical model concept. Understanding the concept Most people have heard that a wide range of colors can be created by the primary colors red, blue, and yellow, if working with paints. Those colors then define a color space. We can specify the amount of red color as the X axis, the amount of blue as the Y axis, and the amount of yellow as the Z axis, giving us a three-dimensional space, wherein every possible color has a unique position.
    [Show full text]
  • Color Difference Delta E - a Survey
    See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/236023905 Color difference Delta E - A survey Article in Machine Graphics and Vision · April 2011 CITATIONS READS 12 8,785 2 authors: Wojciech Mokrzycki Maciej Tatol Cardinal Stefan Wyszynski University in Warsaw University of Warmia and Mazury in Olsztyn 157 PUBLICATIONS 177 CITATIONS 5 PUBLICATIONS 27 CITATIONS SEE PROFILE SEE PROFILE All content following this page was uploaded by Wojciech Mokrzycki on 08 August 2017. The user has requested enhancement of the downloaded file. Colour difference ∆E - A survey Mokrzycki W.S., Tatol M. {mokrzycki,mtatol}@matman.uwm.edu.pl Faculty of Mathematics and Informatics University of Warmia and Mazury, Sloneczna 54, Olsztyn, Poland Preprint submitted to Machine Graphic & Vision, 08:10:2012 1 Contents 1. Introduction 4 2. The concept of color difference and its tolerance 4 2.1. Determinants of color perception . 4 2.2. Difference in color and tolerance for color of product . 5 3. An early period in ∆E formalization 6 3.1. JND units and the ∆EDN formula . 6 3.2. Judd NBS units, Judd ∆EJ and Judd-Hunter ∆ENBS formulas . 6 3.3. Adams chromatic valence color space and the ∆EA formula . 6 3.4. MacAdam ellipses and the ∆EFMCII formula . 8 4. The ANLab model and ∆E formulas 10 4.1. The ANLab model . 10 4.2. The ∆EAN formula . 10 4.3. McLaren ∆EMcL and McDonald ∆EJPC79 formulas . 10 4.4. The Hunter color system and the ∆EH formula . 11 5. ∆E formulas in uniform color spaces 11 5.1.
    [Show full text]
  • NEC Multisync® PA311D Wide Gamut Color Critical Display Designed for Photography and Video Production
    NEC MultiSync® PA311D Wide gamut color critical display designed for photography and video production 1419058943 High resolution and incredible, predictable color accuracy. The 31” MultiSync PA311D is the ultimate desktop display for applications where precise color is essential. The innovative wide-gamut LED backlight provides 100% coverage of Adobe RGB color space and 98% coverage of DCI-P3, enabling more accurate colors to be displayed on screen. Utilizing a high performance IPS LCD panel and backed by a 4 year warranty with Advanced Exchange, the MultiSync PA311D delivers high quality, accurate images simply and beautifully. Impeccable Image Performance The wide-gamut LED LCD backlight combined with NEC’s exclusive SpectraView Engine deliver precise color in every environment. • True 4K resolution (4096 x 2160) offers a high pixel density • Up to 100% coverage of Adobe RGB color space and 98% coverage of DCI-P3 • 10-bit HDMI and DisplayPort inputs display up to 1.07 billion colors out of a palette of 4.3 trillion colors Ultimate Color Management The sophisticated SpectraView Engine provides extensive, intuitive control over color settings. • MultiProfiler software and on-screen controls provide access to thousands of color gamut, gamma, white point, brightness and contrast combinations • Internal 14-bit 3D lookup tables (LUTs) work with optional SpectraViewII color calibration solution for unparalleled color accuracy A Perfect Fit for Your Workspace A Better Workflow Future-proof connectivity, great ergonomics, and VESA mount Exclusive,
    [Show full text]
  • Accurately Reproducing Pantone Colors on Digital Presses
    Accurately Reproducing Pantone Colors on Digital Presses By Anne Howard Graphic Communication Department College of Liberal Arts California Polytechnic State University June 2012 Abstract Anne Howard Graphic Communication Department, June 2012 Advisor: Dr. Xiaoying Rong The purpose of this study was to find out how accurately digital presses reproduce Pantone spot colors. The Pantone Matching System is a printing industry standard for spot colors. Because digital printing is becoming more popular, this study was intended to help designers decide on whether they should print Pantone colors on digital presses and expect to see similar colors on paper as they do on a computer monitor. This study investigated how a Xerox DocuColor 2060, Ricoh Pro C900s, and a Konica Minolta bizhub Press C8000 with default settings could print 45 Pantone colors from the Uncoated Solid color book with only the use of cyan, magenta, yellow and black toner. After creating a profile with a GRACoL target sheet, the 45 colors were printed again, measured and compared to the original Pantone Swatch book. Results from this study showed that the profile helped correct the DocuColor color output, however, the Konica Minolta and Ricoh color outputs generally produced the same as they did without the profile. The Konica Minolta and Ricoh have much newer versions of the EFI Fiery RIPs than the DocuColor so they are more likely to interpret Pantone colors the same way as when a profile is used. If printers are using newer presses, they should expect to see consistent color output of Pantone colors with or without profiles when using default settings.
    [Show full text]
  • Xilinx PG014 Logicore IP Ycrcb to RGB Color-Space Converter V6.00.A, Product Guide
    LogiCORE IP YCrCb to RGB Color-Space Converter v6.00.a Product Guide PG014 July 25, 2012 Table of Contents SECTION I: SUMMARY IP Facts Chapter 1: Overview Feature Summary. 8 Applications . 9 Licensing and Ordering Information. 9 Chapter 2: Product Specification Standards Compliance . 10 Performance. 10 Resource Utilization. 11 Core Interfaces and Register Space . 13 Chapter 3: Designing with the Core General Design Guidelines . 27 Color-Space Conversion Background . 28 Clock, Enable, and Reset Considerations . 34 System Considerations . 36 Chapter 4: C Model Reference Installation and Directory Structure . 38 Using the C-Model . 40 Input and Output Video Structures . 42 Initializing the Input Video Structure . 43 Compiling with the YCrCb to RGB C-Model . 46 YCrCb to RGB Color-Space Converter v6.00.a www.xilinx.com 2 PG014 July 25, 2012 SECTION II: VIVADO DESIGN SUITE Chapter 5: Customizing and Generating the Core Graphical User Interface . 49 Output Generation. 53 Chapter 6: Constraining the Core Required Constraints . 54 Device, Package, and Speed Grade Selections. 54 Clock Frequencies . 54 Clock Management . 54 Clock Placement. 54 Banking . 55 Transceiver Placement . 55 I/O Standard and Placement. 55 SECTION III: ISE DESIGN SUITE Chapter 7: Customizing and Generating the Core Graphical User Interface . 57 Parameter Values in the XCO File . 61 Output Generation. 62 Chapter 8: Constraining the Core Required Constraints . 63 Device, Package, and Speed Grade Selections. 63 Clock Frequencies . 63 Clock Management . 63 Clock Placement. 63 Banking . 64 Transceiver Placement . 64 I/O Standard and Placement. 64 Chapter 9: Detailed Example Design Demonstration Test Bench . 65 Test Bench Structure .
    [Show full text]
  • Predictability of Spot Color Overprints
    Predictability of Spot Color Overprints Robert Chung, Michael Riordan, and Sri Prakhya Rochester Institute of Technology School of Print Media 69 Lomb Memorial Drive, Rochester, NY 14623, USA emails: [email protected], [email protected], [email protected] Keywords spot color, overprint, color management, portability, predictability Abstract Pre-media software packages, e.g., Adobe Illustrator, do amazing things. They give designers endless choices of how line, area, color, and transparency can interact with one another while providing the display that simulates printed results. Most prepress practitioners are thrilled with pre-media software when working with process colors. This research encountered a color management gap in pre-media software’s ability to predict spot color overprint accurately between display and print. In order to understand the problem, this paper (1) describes the concepts of color portability and color predictability in the context of color management, (2) describes an experimental set-up whereby display and print are viewed under bright viewing surround, (3) conducts display-to-print comparison of process color patches, (4) conducts display-to-print comparison of spot color solids, and, finally, (5) conducts display-to-print comparison of spot color overprints. In doing so, this research points out why the display-to-print match works for process colors, and fails for spot color overprints. Like Genie out of the bottle, there is no turning back nor quick fix to reconcile the problem with predictability of spot color overprints in pre-media software for some time to come. 1. Introduction Color portability is a key concept in ICC color management.
    [Show full text]
  • Computational RYB Color Model and Its Applications
    IIEEJ Transactions on Image Electronics and Visual Computing Vol.5 No.2 （2017） -- Special Issue on Application-Based Image Processing Technologies -- Computational RYB Color Model and its Applications Junichi SUGITA† (Member), Tokiichiro TAKAHASHI†† (Member) †Tokyo Healthcare University, ††Tokyo Denki University/UEI Research <Summary> The red-yellow-blue (RYB) color model is a subtractive model based on pigment color mixing and is widely used in art education. In the RYB color model, red, yellow, and blue are defined as the primary colors. In this study, we apply this model to computers by formulating a conversion between the red-green-blue (RGB) and RYB color spaces. In addition, we present a class of compositing methods in the RYB color space. Moreover, we prescribe the appropriate uses of these compo- siting methods in different situations. By using RYB color compositing, paint-like compositing can be easily achieved. We also verified the effectiveness of our proposed method by using several experiments and demonstrated its application on the basis of RYB color compositing. Keywords: RYB, RGB, CMY(K), color model, color space, color compositing man perception system and computer displays, most com- 1. Introduction puter applications use the red-green-blue (RGB) color mod- Most people have had the experience of creating an arbi- el3); however, this model is not comprehensible for many trary color by mixing different color pigments on a palette or people who not trained in the RGB color model because of a canvas. The red-yellow-blue (RYB) color model proposed its use of additive color mixing. As shown in Fig.
    [Show full text]
  • Color Appearance Models Today's Topic
    Color Appearance Models Arjun Satish Mitsunobu Sugimoto 1 Today's topic Color Appearance Models CIELAB The Nayatani et al. Model The Hunt Model The RLAB Model 2 1 Terminology recap Color Hue Brightness/Lightness Colorfulness/Chroma Saturation 3 Color Attribute of visual perception consisting of any combination of chromatic and achromatic content. Chromatic name Achromatic name others 4 2 Hue Attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors Often refers red, green, blue, and yellow 5 Brightness Attribute of a visual sensation according to which an area appears to emit more or less light. Absolute level of the perception 6 3 Lightness The brightness of an area judged as a ratio to the brightness of a similarly illuminated area that appears to be white Relative amount of light reflected, or relative brightness normalized for changes in the illumination and view conditions 7 Colorfulness Attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic 8 4 Chroma Colorfulness of an area judged as a ratio of the brightness of a similarly illuminated area that appears white Relationship between colorfulness and chroma is similar to relationship between brightness and lightness 9 Saturation Colorfulness of an area judged as a ratio to its brightness Chroma – ratio to white Saturation – ratio to its brightness 10 5 Definition of Color Appearance Model so much description of color such as: wavelength, cone response, tristimulus values, chromaticity coordinates, color spaces, … it is difficult to distinguish them correctly We need a model which makes them straightforward 11 Definition of Color Appearance Model CIE Technical Committee 1-34 (TC1-34) (Comission Internationale de l'Eclairage) They agreed on the following definition: A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue.
    [Show full text]
  • NEC Multisync® PA271Q Wide Gamut Color Critical Display Designed for Photography and Video Production
    NEC MultiSync® PA271Q Wide gamut color critical display designed for photography and video production 1419058943 High resolution and incredible, predictable color accuracy. The 27” MultiSync PA271Q is the ultimate desktop display for applications where precise color is essential. The innovative wide-gamut W-LED backlight provides 98.5% coverage of the Adobe RGB color space and 81.1% of Rec 2020 color space, enabling more accurate colors to be displayed on screen. Utilizing a high performance IPS LCD panel and backed by a 4 year warranty with Advanced Exchange, the MultiSync PA271Q delivers high quality, accurate images simply and beautifully. Impeccable Image Performance The wide-gamut W-LED backlight combined with NEC’s exclusive SpectraView Engine deliver precise color in every environment. • QHD resolution (2560 x 1440) offers a high pixel density • Up to 98.5% coverage of the Adobe RGB color space and accurate 100% coverage of sRGB • 10-bit HDMI and DisplayPort inputs display up to 1.07 billion colors out of a palette of 4.3 trillion colors Ultimate Color Management The sophisticated SpectraView Engine provides extensive, intuitive control over color settings. • MultiProfiler software and on-screen controls provide access to thousands of color gamut, gamma, white point, brightness and contrast combinations • Internal 14-bit 3D lookup tables (LUTs) work with optional SpectraViewII color calibration solution for unparalleled color accuracy A Perfect Fit for Your Workspace A Better Workflow Future-proof connectivity, great ergonomics, and VESA mount Exclusive, innovative features can eliminate steps in a color editing workflow, saving time and cabinets to fit every desk and office environment.
    [Show full text]
  • ARC Laboratory Handbook. Vol. 5 Colour: Specification and Measurement
    Andrea Urland CONSERVATION OF ARCHITECTURAL HERITAGE, OFARCHITECTURALHERITAGE, CONSERVATION Colour Specification andmeasurement HISTORIC STRUCTURESANDMATERIALS UNESCO ICCROM WHC VOLUME ARC 5 /99 LABORATCOROY HLANODBOUOKR The ICCROM ARC Laboratory Handbook is intended to assist professionals working in the field of conserva- tion of architectural heritage and historic structures. It has been prepared mainly for architects and engineers, but may also be relevant for conservator-restorers or archaeologists. It aims to: - offer an overview of each problem area combined with laboratory practicals and case studies; - describe some of the most widely used practices and illustrate the various approaches to the analysis of materials and their deterioration; - facilitate interdisciplinary teamwork among scientists and other professionals involved in the conservation process. The Handbook has evolved from lecture and laboratory handouts that have been developed for the ICCROM training programmes. It has been devised within the framework of the current courses, principally the International Refresher Course on Conservation of Architectural Heritage and Historic Structures (ARC). The general layout of each volume is as follows: introductory information, explanations of scientific termi- nology, the most common problems met, types of analysis, laboratory tests, case studies and bibliography. The concept behind the Handbook is modular and it has been purposely structured as a series of independent volumes to allow: - authors to periodically update the
    [Show full text]
  • Measuring Perceived Color Difference Using YIQ NTSC Transmission Color Space in Mobile Applications
    Programación Matemática y Software (2010) Vol.2. Num. 2. Dirección de Reservas de Derecho: 04-2009-011611475800-102 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLÓGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen. En este trabajo varias formulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las formulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes formulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las formulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real. Abstract. An alternative color difference formulas are presented for measuring the perceived difference between two color samples defined in YIQ color space.
    [Show full text]